More and more surveillance cameras are used by people to guarantee security in cities, transportation, public places and other aspects, and car cameras are used to improve the safety of driving. However, due to low-illumination conditions (e.g., night, backlight, indoor, etc.), the performance of such cameras will be greatly reduce, so that the visibility of images and videos decreases, and it is difficult to identify key characters, objects and other information. The images taken in the above cases are called low-illumination images, and the low-illumination images have various kinds of noise, which becomes more prominent after image enhancement, reducing recognition of objects in the image. The subjective feelings of people are significantly compromised.
In traditional image enhancement techniques (such as defogging and low-illumination enhancement), the original noises in images will be greatly enhanced after image processing, and a lot of noises such as Color noise and Luma noise will occur in images. These noises cannot be effectively filtered with traditional image enhancement techniques.
The following two problems cannot be solved with traditional noise reduction methods:
1) Various types of Color noise can be effectively filtered, and the color saturation of object can be retained at the same time.
2) Various types of Luma noise can be filtered, and the image details can be retained at the same time.